Analysis of the results of on-line electric power system state estimation

Glazunova, Anna; Kolosok, Irina

doi:10.1109/ptc.2011.6019156

Cited by 2 publications

(1 citation statement)

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“…Besides to measurement model as for static State Estimation the Dynamic State Estimation considers a signal model too [24] Models containing too much variables do not always improve the quality and reliability of results. For a short-term forecasting of state parameters we use linear dynamic models [25]. In the dynamic models random fluctuations of state parameters are represented as a stationary Gaussian process (white noise) and all changes in state parameters are considered as random fluctuations and are taken into account.…”

Section: B Dynamic State Estimationmentioning

confidence: 99%

Coordinated monitoring of large scale interconnected power systems

Gorner¹,

Rehtanz²,

Kolosok

et al. 2013

2013 IEEE Power &Amp; Energy Society General Meeting

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This paper presents the final result of a joint EU and Russian project called ICOEUR (Intelligent Coordination of Operation and Emergency Control of EU and Russian Power Grids). The presented results are about the extension and interconnection of large scale power systems from the perspective of monitoring and control. In this paper the authors identify algorithms and monitoring technologies for the identification of full system states and dynamic behavior of system. The algorithms are validated with realistic models from the European and Russian power system. The results lead to a coordination platform which applies Distributed and Dynamic State Estimation in order to manage cross border flows.

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Section: B Dynamic State Estimationmentioning

confidence: 99%